init

app.py

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+import cv2
+import gradio as gr
+import numpy as np
+import onnxruntime
+import requests
+from huggingface_hub import hf_hub_download
+from PIL import Image
+from rembg import remove
+
+
+# Get x_scale_factor & y_scale_factor to resize image
+def get_scale_factor(im_h, im_w, ref_size=512):
+
+    if max(im_h, im_w) < ref_size or min(im_h, im_w) > ref_size:
+        if im_w >= im_h:
+            im_rh = ref_size
+            im_rw = int(im_w / im_h * ref_size)
+        elif im_w < im_h:
+            im_rw = ref_size
+            im_rh = int(im_h / im_w * ref_size)
+    else:
+        im_rh = im_h
+        im_rw = im_w
+
+    im_rw = im_rw - im_rw % 32
+    im_rh = im_rh - im_rh % 32
+
+    x_scale_factor = im_rw / im_w
+    y_scale_factor = im_rh / im_h
+
+    return x_scale_factor, y_scale_factor
+
+
+MODEL_PATH = hf_hub_download('nateraw/background-remover-files', 'modnet.onnx', repo_type='dataset')
+
+
+def main(image_path, threshold):
+
+    # read image
+    im = cv2.imread(image_path)
+    im = cv2.cvtColor(im, cv2.COLOR_BGR2RGB)
+
+    # unify image channels to 3
+    if len(im.shape) == 2:
+        im = im[:, :, None]
+    if im.shape[2] == 1:
+        im = np.repeat(im, 3, axis=2)
+    elif im.shape[2] == 4:
+        im = im[:, :, 0:3]
+
+    # normalize values to scale it between -1 to 1
+    im = (im - 127.5) / 127.5
+
+    im_h, im_w, im_c = im.shape
+    x, y = get_scale_factor(im_h, im_w)
+
+    # resize image
+    im = cv2.resize(im, None, fx=x, fy=y, interpolation=cv2.INTER_AREA)
+
+    # prepare input shape
+    im = np.transpose(im)
+    im = np.swapaxes(im, 1, 2)
+    im = np.expand_dims(im, axis=0).astype('float32')
+
+    # Initialize session and get prediction
+    session = onnxruntime.InferenceSession(MODEL_PATH, None)
+    input_name = session.get_inputs()[0].name
+    output_name = session.get_outputs()[0].name
+    result = session.run([output_name], {input_name: im})
+
+    # refine matte
+    matte = (np.squeeze(result[0]) * 255).astype('uint8')
+    matte = cv2.resize(matte, dsize=(im_w, im_h), interpolation=cv2.INTER_AREA)
+
+    # HACK - Could probably just convert this to PIL instead of writing
+    cv2.imwrite('out.png', matte)
+
+    image = Image.open(image_path)
+    matte = Image.open('out.png')
+
+    # obtain predicted foreground
+    image = np.asarray(image)
+    if len(image.shape) == 2:
+        image = image[:, :, None]
+    if image.shape[2] == 1:
+        image = np.repeat(image, 3, axis=2)
+    elif image.shape[2] == 4:
+        image = image[:, :, 0:3]
+
+    b, g, r = cv2.split(image)
+
+    mask = np.asarray(matte)
+    a = np.ones(mask.shape, dtype='uint8') * 255
+    alpha_im = cv2.merge([b, g, r, a], 4)
+    bg = np.zeros(alpha_im.shape)
+    new_mask = np.stack([mask, mask, mask, mask], axis=2)
+    foreground = np.where(new_mask > threshold, alpha_im, bg).astype(np.uint8)
+
+    bg_remove1 = Image.fromarray(foreground)  # Variant 1
+    bg_remove2 = remove(Image.open(image_path)) # Variant 2
+    return [bg_remove1, bg_remove2]
+
+
+title = "MODNet Background Remover"
+description = "Gradio demo for MODNet, a model that can remove the background from a given image. To use it, simply upload your image, or click one of the examples to load them. Read more at the links below."
+article = "<div style='text-align: center;'> <a href='https://github.com/ZHKKKe/MODNet' target='_blank'>Github Repo</a> | <a href='https://arxiv.org/abs/2011.11961' target='_blank'>MODNet: Real-Time Trimap-Free Portrait Matting via Objective Decomposition</a> </div>"
+
+interface = gr.Interface(
+    fn=main,
+    inputs=[
+        gr.Image(type='filepath'),
+        gr.Slider(minimum=0, maximum=250, value=100, step=5, label='Mask Cutoff Threshold'),
+    ],
+    outputs=gr.Gallery(format="png"),
+    title=title,
+    description=description,
+    article=article,
+    flagging_mode="never",
+    ).queue().launch(show_error=True)

packages.txt

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+ffmpeg 
+libsm6 
+libxext6

requirements.txt

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+onnxruntime
+onnx
+opencv-python
+numpy
+huggingface_hub
+rembg